Abstract
In Geometric Semantic Genetic Programming (GSGP), genetic operators directly work at the level of semantics rather than syntax. It provides many advantages, including much higher quality of resulting individuals (in terms of error) in comparison with a common genetic programming. However, GSGP produces extremely huge solutions that could be difficult to apply in systems with limited resources such as embedded systems. We propose Subtree Cartesian Genetic Programming (SCGP) – a method capable of reducing the number of nodes in the trees generated by GSGP. SCGP executes a common Cartesian Genetic Programming (CGP) on all elementary subtrees created by GSGP and on various compositions of these optimized subtrees in order to create one compact representation of the original program. SCGP does not guarantee the (exact) semantic equivalence between the CGP individuals and the GSGP subtrees, but the user can define conditions when a particular CGP individual is acceptable. We evaluated SCGP on four common symbolic regression benchmark problems and the obtained node reduction is from 92.4% to 99.9%.
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Notes
- 1.
As all these trees are used in a single resulting solution, we will call them subtrees.
References
Moraglio, A., Krawiec, K., Johnson, C.G.: Geometric semantic genetic programming. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds.) PPSN 2012. LNCS, vol. 7491, pp. 21–31. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32937-1_3
Vanneschi, L., Silva, S., Castelli, M., Manzoni, L.: Geometric semantic genetic programming for real life applications. In: Riolo, R., Moore, J.H., Kotanchek, M. (eds.) Genetic Programming Theory and Practice XI. GEC, pp. 191–209. Springer, New York (2014). https://doi.org/10.1007/978-1-4939-0375-7_11
Martins, J.F.B.S., Oliveira, L.O.V.B., Miranda, L.F., Casadei, F., Pappa, G.L.: Solving the exponential growth of symbolic regression trees in geometric semantic genetic programming. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, 15–19 July 2018, Kyoto, Japan, pp. 1151–1158. ACM (2018)
Miller, J.F., Job, D., Vassilev, V.K.: Principles in the evolutionary design of digital circuits - part I. Genet. Program. Evolvable Mach. 1(1), 8–35 (2000)
Nguyen, Q.U., Pham, T.A., Nguyen, X.H., McDermott, J.: Subtree semantic geometric crossover for genetic programming. Genet. Program. Evolvable Mach. 17(1), 25–53 (2016)
Castelli, M., Silva, S., Vanneschi, L.: A C++ framework for geometric semantic genetic programming. Genet. Program. Evolvable Mach. 16(1), 73–81 (2015). https://doi.org/10.1007/s10710-014-9218-0
Miller, J.F.: Cartesian Genetic Programming. Springer, Berlin (2011). https://doi.org/10.1007/978-3-642-17310-3
Archetti, F., Lanzeni, S., Messina, E., Vanneschi, L.: Genetic programming for computational pharmacokinetics in drug discovery and development. Genet. Program. Evolvable Mach. 8(4), 413–432 (2007). https://doi.org/10.1007/s10710-007-9040-z
Vanneschi, L.: An introduction to geometric semantic genetic programming. In: Schütze, O., Trujillo, L., Legrand, P., Maldonado, Y. (eds.) NEO 2015. SCI, vol. 663, pp. 3–42. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-44003-3_1
Dua, D., Karra Taniskidou, E.: UCI machine learning repository (2017). http://archive.ics.uci.edu/ml
Castelli, M., Trujillo, L., Vanneschi, L., Silva, S., Z.-Flores, E., Legrand, P.: Geometric semantic genetic programming with local search. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2015, 11–15 July 2015, Madrid, Spain, pp. 999–1006. ACM (2015)
Acknowledgments
This work was supported by the Ministry of Education, Youth and Sports, under the INTER-COST project LTC 18053. The authors would like to thank Dr. Mauro Castelli for his support regarding the C++ framework for GSGP.
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Koncal, O., Sekanina, L. (2019). Cartesian Genetic Programming as an Optimizer of Programs Evolved with Geometric Semantic Genetic Programming. In: Sekanina, L., Hu, T., Lourenço, N., Richter, H., García-Sánchez, P. (eds) Genetic Programming. EuroGP 2019. Lecture Notes in Computer Science(), vol 11451. Springer, Cham. https://doi.org/10.1007/978-3-030-16670-0_7
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